Wavelet-based salient points for image retrieval

Loupias, Etienne; Sebe, Nicu; Bres, Stéphane; Jolion, Jean-Michel

doi:10.1109/icip.2000.899469

Cited by 114 publications

(88 citation statements)

References 10 publications

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“…Song et al (Song, et al, 2006) have proposed the use of wavelet to identify the salient points in colour image using the three bands RGB. The use of wavelet may give good results in a non-homogeneous image, like the cameraman example that was used in (Loupias, et al, 2000), but it will not give that good results when applied on image with high texture.…”

Section: Previous Workmentioning

confidence: 99%

“…Loupias et al have used orthogonal Haar wavelet in extracting the salient points (Loupias, et al, 2000). Song et al (Song, et al, 2006) have proposed the use of wavelet to identify the salient points in colour image using the three bands RGB.…”

Section: Previous Workmentioning

confidence: 99%

“…Wavelet was used by many authors such as in (Loupias, et al, 2000), (Tian, et al, 2001), (Song, et al, 2006), (Lin & Yang, 2007), and (Arivazhagan & Shebiah, 2009) In this paper, we shall introduce a robust and general saliency extraction method that is fully automated and does not need any intervention from the user. In addition, it does need any pre-knowledge about the image.…”

Section: Previous Workmentioning

confidence: 99%

“…Wavelet was used by many authors such as in (Loupias, et al, 2000), (Tian, et al, 2001), (Song, et al, 2006), (Lin & Yang, 2007), and (Arivazhagan & Shebiah, 2009) in which they have applied the principles of wavelet transform to extract the salient points. Loupias et al have used orthogonal Haar wavelet in extracting the salient points (Loupias, et al, 2000).…”

Section: Previous Workmentioning

confidence: 99%

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Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

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IntroductionSalient points, objects, or regions extraction is still a challenging topic of research and its definition is still very general. Different literature have defined the saliency in different ways. In general and based on human vision system HVS, the salient object is the object that may capture the attention or attract the HVS. HVS uses two stages to identify the objects, preattentive and attentive stages (Kadir & Brady, 2001).In pre-attentive stage, the local regions of image that present spatial discontinuity are detected. In the attentive stage, relationships between these regions are found, and grouped together. The rest of the paper will be organized as follows: previous work is presented in section 2. In Section 3, we will explain the proposed saliency extraction technique based on the irregularity of the regions in an image. In section 44, a new automatic thresholding technique will be proposed. Points clustering and region merging will be discussed in section 5. Section 6 will review the saliency evaluation technique and introduce a new method that is suitable to the application at hand. In section 7 we will discuss the experimental results and the optimum window size selection. Finally, conclusions will be derived in section 8. 2 Previous WorkWavelet was used by many authors such as in (Loupias, et al., 2000), (Tian, et al., 2001), (Song, et al., 2006), (Lin & Yang, 2007), and (Arivazhagan & Shebiah, 2009) In this paper, we shall introduce a robust and general saliency extraction method that is fully automated and does not need any intervention from the user. In addition, it does need any pre-knowledge about the image. Irregularity as a Measure of SaliencyIn this Section, we shall discuss the proposed technique. First, we shall define the saliency as any irregular region in the image with nature differs from the nature of the image as a whole; for example, a region with texture in a uniform region, or vice versa, thus we shall define irregularity as the main measure of saliency.Let ℐ( , ) be the image intensity at position ( , ), where = 1, 2, … . , , = 1,2, … , , with and are the image width and height respectively. The image is represented as a set of pixels and is defined as:Where p xy is the pixel's value at location ( , ) and ℕ is the set of natural numbers. Furthermore, we shall define the set ℙ = { | ⊆ } as the power set of .According to the application at hands, the main constrain is that; all the elements of a given subset should belong to a connected region which means they should be in a given neighbouring area.The sub-image, which we shall refer to as a region, is a subset of the image with elements that belong to the same region. The size of this sub-image is × ℎ.Considering that the regions are disjoint, they can be defined as:Assuming that there is no overlapping between the adjacent regions, the total number of regions is equal to × ℎ . In most cases there should be overlapping between the moving windows, if we denote the overlapping value by , then the total numbers of wind...

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

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“…Local descriptors (e.g. salient points [2]) are based on a subset of the pixels of the image while global descriptors give information about the image as a whole (e.g. color histograms [5]).…”

Section: Similarity In Image Processingmentioning

confidence: 99%

Sparse Multiscale Patches for Image Processing

Piro

Anthoine

Debreuve

et al. 2009

Emerging Trends in Visual Computing

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Abstract. This paper presents a framework to define an objective measure of the similarity (or dissimilarity) between two images for image processing. The problem is twofold: 1) define a set of features that capture the information contained in the image relevant for the given task and 2) define a similarity measure in this feature space. In this paper, we propose a feature space as well as a statistical measure on this space. Our feature space is based on a global descriptor of the image in a multiscale transformed domain. After decomposition into a Laplacian pyramid, the coefficients are arranged in intrascale/interscale/interchannel patches which reflect the dependencies of neighboring coefficients in presence of specific structures or textures. At each scale, the probability density function (pdf) of these patches is used as a descriptor of the relevant information. Because of the sparsity of the multiscale transform, the most significant patches, called Sparse Multiscale Patches (SMP), describe efficiently these pdfs. We propose a statistical measure (the Kullback-Leibler divergence) based on the comparison of these probability density functions. Interestingly, this measure is estimated via the nonparametric, k-th nearest neighbor framework without explicitly building the pdfs. This framework is applied to a query-by-example image retrieval method. Experiments on two publicly available databases showed the potential of our SMP approach for this task. In particular, it performed comparably to a SIFT -based retrieval method and two versions of a fuzzy segmentation-based method (the UFM and CLUE methods), and it exhibited some robustness to different geometric and radiometric deformations of the images.

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